In [1]:
#restart the kernel after installation
!pip install pandas-profiling --quiet
In [2]:
medical_charges_url = 'https://raw.githubusercontent.com/JovianML/opendatasets/master/data/medical-charges.csv'
In [3]:
from urllib.request import urlretrieve
In [6]:
urlretrieve(medical_charges_url, 'medical-charges.csv')
Out[6]:
('medical-charges.csv', <http.client.HTTPMessage at 0x18c9ef25cd0>)
In [7]:
import pandas as pd
In [8]:
medical_df = pd.read_csv('medical.csv')
In [9]:
medical_df
Out[9]:
age sex bmi children smoker region charges
0 19 female 27.900 0 yes southwest 16884.92400
1 18 male 33.770 1 no southeast 1725.55230
2 28 male 33.000 3 no southeast 4449.46200
3 33 male 22.705 0 no northwest 21984.47061
4 32 male 28.880 0 no northwest 3866.85520
... ... ... ... ... ... ... ...
1333 50 male 30.970 3 no northwest 10600.54830
1334 18 female 31.920 0 no northeast 2205.98080
1335 18 female 36.850 0 no southeast 1629.83350
1336 21 female 25.800 0 no southwest 2007.94500
1337 61 female 29.070 0 yes northwest 29141.36030

1338 rows × 7 columns

In [10]:
medical_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1338 entries, 0 to 1337
Data columns (total 7 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   age       1338 non-null   int64  
 1   sex       1338 non-null   object 
 2   bmi       1338 non-null   float64
 3   children  1338 non-null   int64  
 4   smoker    1338 non-null   object 
 5   region    1338 non-null   object 
 6   charges   1338 non-null   float64
dtypes: float64(2), int64(2), object(3)
memory usage: 73.3+ KB
In [11]:
medical_df.describe()
Out[11]:
age bmi children charges
count 1338.000000 1338.000000 1338.000000 1338.000000
mean 39.207025 30.663397 1.094918 13270.422265
std 14.049960 6.098187 1.205493 12110.011237
min 18.000000 15.960000 0.000000 1121.873900
25% 27.000000 26.296250 0.000000 4740.287150
50% 39.000000 30.400000 1.000000 9382.033000
75% 51.000000 34.693750 2.000000 16639.912515
max 64.000000 53.130000 5.000000 63770.428010
In [15]:
!pip install plotly matplotlib seaborn --quiet
In [16]:
import plotly.express as px
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
In [17]:
sns.set_style('darkgrid')
matplotlib.rcParams['font.size'] = 14
matplotlib.rcParams['figure.figsize'] = (10, 6)
matplotlib.rcParams['figure.facecolor'] = '#00000000'
In [18]:
medical_df.age.describe()
Out[18]:
count    1338.000000
mean       39.207025
std        14.049960
min        18.000000
25%        27.000000
50%        39.000000
75%        51.000000
max        64.000000
Name: age, dtype: float64
In [19]:
fig = px.histogram(medical_df, 
                   x='age', 
                   marginal='box', 
                   nbins=47, 
                   title='Distribution of Age')
fig.update_layout(bargap=0.1)
fig.show()
2030405060010203040506070
Distribution of Ageagecount
plotly-logomark
In [20]:
fig = px.histogram(medical_df, 
                   x='bmi', 
                   marginal='box', 
                   color_discrete_sequence=['red'], 
                   title='Distribution of BMI (Body Mass Index)')
fig.update_layout(bargap=0.1)
fig.show()
152025303540455055020406080
Distribution of BMI (Body Mass Index)bmicount
plotly-logomark
In [22]:
fig = px.histogram(medical_df, 
                   x='charges', 
                   marginal='box', 
                   color_discrete_sequence=['green','grey'], 
                   title='Annual medical charges')
fig.update_layout(bargap=0.1)
fig.show()
010k20k30k40k50k60k050100150
Annual medical chargeschargescount
plotly-logomark
In [23]:
medical_df.smoker.value_counts()
Out[23]:
smoker
no     1064
yes     274
Name: count, dtype: int64
In [24]:
px.histogram(medical_df, x='smoker', color='sex', title='Smoker')
yesno02004006008001000
sexfemalemaleSmokersmokercount
plotly-logomark
In [25]:
fig = px.scatter(medical_df, 
                 x='age', 
                 y='charges', 
                 color='smoker', 
                 opacity=0.8, 
                 hover_data=['sex'], 
                 title='Age vs. Charges')
fig.update_traces(marker_size=5)
fig.show()
2030405060010k20k30k40k50k60k
smokeryesnoAge vs. Chargesagecharges
plotly-logomark
In [26]:
fig = px.scatter(medical_df, 
                 x='bmi', 
                 y='charges', 
                 color='smoker', 
                 opacity=0.8, 
                 hover_data=['sex'], 
                 title='BMI vs. Charges')
fig.update_traces(marker_size=5)
fig.show()
152025303540455055010k20k30k40k50k60k
smokeryesnoBMI vs. Chargesbmicharges
plotly-logomark
In [27]:
medical_df.charges.corr(medical_df.age)
Out[27]:
0.29900819333064765
In [28]:
medical_df.charges.corr(medical_df.bmi)
Out[28]:
0.19834096883362892
In [29]:
smoker_values = {'no': 0, 'yes': 1}
smoker_numeric = medical_df.smoker.map(smoker_values)
medical_df.charges.corr(smoker_numeric)
Out[29]:
0.7872514304984772
In [34]:
non_smoker_df = medical_df[medical_df.smoker == 'no']
In [35]:
plt.title('Age vs. Charges')
sns.scatterplot(data=non_smoker_df, x='age', y='charges', alpha=0.7, s=15);
In [36]:
def estimate_charges(age, w, b):
    return w * age + b
In [37]:
w = 50
b = 100
In [38]:
ages = non_smoker_df.age
estimated_charges = estimate_charges(ages, w, b)
In [39]:
plt.plot(ages, estimated_charges, 'r-o');
plt.xlabel('Age');
plt.ylabel('Estimated Charges');
In [40]:
target = non_smoker_df.charges

plt.plot(ages,estimated_charges, 'r', alpha=0.9);
plt.scatter(ages,target,s=8,alpha=0.8);
plt.xlabel('age');
plt.ylabel('charges')
plt.legend(['estimate','actual']);
In [48]:
def try_parameters(w, b):
    ages = non_smoker_df.age
    target = non_smoker_df.charges
    
    estimated_charges = estimate_charges(ages, w, b)
    
    plt.plot(ages, estimated_charges, 'r', alpha=0.9);
    plt.scatter(ages, target, s=8,alpha=0.8);
    plt.xlabel('Age');
    plt.ylabel('Charges')
    plt.legend(['Estimate', 'Actual']);
In [49]:
try_parameters(60,200)
In [50]:
try_parameters(400,5000)
In [51]:
pip install numpy --quiet

Note: you may need to restart the kernel to use updated packages.
In [52]:
import numpy as np
In [53]:
def rmse(targets, predictions):
    return np.sqrt(np.mean(np.square(targets - predictions)))
In [54]:
w = 50
b = 100
In [55]:
try_parameters(w, b)
In [56]:
targets = non_smoker_df['charges']
predicted = estimate_charges(non_smoker_df.age, w, b)
In [57]:
rmse(targets, predicted)
Out[57]:
8461.949562575493
In [58]:
def try_parameters(w, b):
    ages = non_smoker_df.age
    target = non_smoker_df.charges
    predictions = estimate_charges(ages, w, b)
    
    plt.plot(ages, predictions, 'r', alpha=0.9);
    plt.scatter(ages, target, s=8,alpha=0.8);
    plt.xlabel('Age');
    plt.ylabel('Charges')
    plt.legend(['Prediction', 'Actual']);
    
    loss = rmse(target, predictions)
    print("RMSE Loss: ", loss)
In [59]:
try_parameters(50, 100)

RMSE Loss:  8461.949562575493